Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
J Chromatogr A. 2023 Nov 22;1711:464437. doi: 10.1016/j.chroma.2023.464437. Epub 2023 Oct 11.
Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.
多模式色谱技术因其能够选择性地捕获和分离目标分子而成为一种很有前途的抗体纯化技术。然而，由于蛋白质-配体相互作用的复杂性，色谱参数的优化仍然是一个挑战。为了解决这个问题，高效的预测工具对于多模式色谱过程的开发和优化至关重要。在这项研究中，我们介绍了一种基于抗体氨基酸序列预测多模式色谱中抗体洗脱行为的方法。我们分析了总共 64 种全长抗体，包括 IgG1、IgG4 和 IgG 样多特异性形式，它们在阴离子混合模式树脂 Capto adhere 上用线性 pH 梯度从 pH9.0 洗脱至 pH4.0。构建了同源模型，并为每个分子计算了 1312 个抗体特异性物理化学描述符。我们的分析确定了多模式抗体相互作用的六个关键结构特征，这些特征与洗脱行为相关，强调了抗体可变区。结果表明，我们的方法可以预测多种抗体和抗体形式的 pH 梯度洗脱，测试集 R²为 0.898。开发的模型可以通过预测多模式洗脱的初始条件为过程开发提供信息，从而减少过程优化中的反复试验。此外，该模型还有望通过筛选符合标准化纯化条件的目标抗体来实现虚拟可制造性评估。总之，本研究强调了使用基于结构的预测来增强生物制药行业中抗体纯化的可行性。这种方法可以提高效率和降低成本，同时增加对工艺的理解。
